Kontagent :: Creating a Data-Driven Culture

Now that you have the data, what's the plan? Using customer data to understand and optimize your social or mobile game can produce huge returns. But, there are also dangers of relying too heavily on
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Now that you have the data, what's the plan? Using customer data to understand and optimize your social or mobile game can produce huge returns. But, there are also dangers of relying too heavily on data without the proper level of controls, data science and overall process. Fortunately, there are now tools, technology and talent available in the market that are enabling forces for studios who want to be more data-driven. This session will analyze what it takes to become a data-driven organization, and look at some lessons learned from our experience working with some of the top grossing social and mobile game studios.

Cover page. Design should be Kontagent, with a touch of skiing, maybe a mountain in the background?

Before we start, I’d like to get a sense of how “data driven” the audience is, or how data driven you think you are. So, here’s a quick test to allow me…and you…to see just where you score. I’d like to give credit to Sheridan Hitchens from Kabam for developing this list of questions, and the exercise. We’ve taken the liberty of adding some of our own questions as well.

Pretty insightful way of looking at data-driven design. Seems like a novel idea, but it’s actually a lesson many have learned the hard way.

OK, let’s get right into it – here are our 6 rules of running a data-driven organization, based on our experience working with hundreds of top developers across thousands of apps.Skiers responsibility code

Here’s the bottom line: You’ve got to know where you’re going in order to get there. And…It helps to have a map to help you. I don’t care how amazing your data infrastructure is, from processing to the visualization layer…it doesn’t matter if you don’t know what metrics matter, and which don’t. Data is aplenty, but which metrics are most important? On two levels: - Which metrics are important at various stages of your game lifecycle? The KPI’s evolve as your game matures. - Which metrics are important to various departments within your organization?

At Kontagent, we’ve simplified the world of social and mobile analytics by focusing our dashboard on SEVEN metrics we believe will serve very well as your trail map. I’ll take more later in this lecture about which metrics are most relevant at various points in your game’s lifecycle. However, for now, consider this your trailmap that will help you get from point A to point B. Now, at some point you will likely graduate from this fairly basic guide, and in looking at the audience I know many of you have already. You’ll want greater flexibility, and you’ll run custom cohort analyses on acquisition sources and in-game engagement behaviors. You’ll want to mine your user data to find important nuggets of information that you can use to tune Acquisition, Engagement and Monetization efforts. When you get there, let us know – we’ll give you the advanced trail map that show you all the “off piste” terrain. But, for those of you just launching your game, or still figuring out how to truly use data to inform better decisions, use this guide to start.

In our experience, those organizations that create a process for enabling each organization across the company to access the data that is important to them, are the most successful. From the C-level to the producer to the marketer to the engineers to the designers, if you use a universal data set that is readily accessible, and enables your company to make decisions in lock-step, you will sincerely have a competitive advantage.Now, there will always be cases where you as a marketer or you as a developer will look at your own data sets, and make independent decisions for acquisition or game tuning. That’s quite alright. But, the good stuff happens when the entire org can peel back the onion together and investigate why a certain trend is going a certain direction, and dig layers deeper to find the root causes.Single point of failure…experiential knowledge can be lost if you get hit by a snowcat.

At Kontagent, we often use the statement, “Measure and Optimize Your Customer Economics at Every Stage in the Game, Across every Level of the Organization”. It’s kind of our tagline. What’s so important about that tagline is that it puts the focus on optimizing the economics of your game…not just one piece of the pie, but understanding how all the levers that drive towards greater profitability can be improved…from CAC to ARPU.

Here’s an example. One of our clients’ CEO’s recently saw a trend in their Kontagent dashboard that he didn’t like. The game was doing well enough, but it had hit a roadblock in terms of their ability to scale their acquisition efforts. They were finding it incredibly difficult to increase their DAU’s without substantially increasing their CAC, and this was not scalable. The retention trend led the CEO to ask questions that permeated across every department within the organization. Marketing was forced to look deeper into their cohorts of users who installed the game, and see if there were any cohorts that weren’t coming back. The production team was forced to look more closely at each event in their game to see if there was one that was scaring players away, and causing higher drop off rates. The engineers used our real-time monitoring dashboard to see if any events weren’t firing, if there was a lag in any of the load time, or even if any of their servers were underperforming.Data is a great motivator for an entire organization to look for root causes, and make constant improvements to the performance of their game, and the lifetime value of their users.Cohort analysis is really at the center of all of this…which user segments are most engaged, and which aren’t? males vs. females, different age groups? Different in-game behaviors? Main question around custom cohorting.Was there a drop-off at some popular stage in the game?Was the virtual economy balance off-kilter?Was the game loading slowly?Which marketing channels were producing the less engaged users?How do we measure and optimize all of the relevant levers that affect retention?

Recently, we published a very well read blog post entitled “Big Data is Useless Without Science”. In the post, our data scientist who wrote it made the case pretty clear that at this stage in the game, the ability to process the data is a commodity. The ability to visualize it is too. It’s figuring out what to do with it that makes all the difference in the world.So, it is absolutely essential to your data-driven business to hire the right talent in-house to work with the data. And, on top of that…and, this is not a pitch for Kontagent, although it is something we obviously strongly believe in…leverage resources that are already available to you by working with a company like Kontagent. Data science is at the heart of everything we do – from the charts we’ve bubbled up to the dashboard to the math PhD’s who help our clients interpret and act on the data to improve their applications to the domain expertise we can bring to the table to offer benchmarks for relative performance gauging.

Lots of options exist in the market today for technology that will help your team process, store and visualize your player data. From Hive/Hadoop combos to Vertica to Kontagent, there are no shortage of alternatives. It’s up to you to assess what you need RIGHT NOW to get the job done in a data-driven business. And, there’s a cost/benefit analysis we encourage all of our customers and prospects to do.

What is the time and costs involved in building out a fully functional data warehouse? What are the ongoing maintenance costs? If we do build an in-house solution, how quickly and how frequently can I get dashboards produced that will help me understand how the business is doing?How do we support a truly data-driven org with multiple audiences? Do we have the expertise to service all the different audiences in our organization, from executive to marketing to product? Is that really a top priority?How long before we outgrow the system, and it needs to be rebuilt? Will a SaaS-delivered system better scale with our business?Who in our organization knows which social/mobile metrics to track today and which ones the industry will find important tomorrow? Will they know how we are doing on a relative basis to our competitors? Can we innovate as quickly as a team of 80+ who have 40+ man years over 4 years of development, and are solely focused on solving this problem?What’s the uptime of a homegrown system? Who will be called when the system goes down at 1am on a Saturday? Should we be focusing our development team on building out important new features and exploring data or on building and maintaining reporting systems? What happens when our data grows by 10x? What if that growth happens overnight? Are we really going to be prepared? Is it worth the risk?Do I understand all the potential points of failure? Do I have a plan for failover? Did I budget for redundancy and/or failover?Can my business afford to lose data? Do I have a long term data storage solution?Infrastructure + Staff + Licensing vs MA: Which is a more economical Analytics solution for my business?

Don’t get caught up looking at the wrong data that will lead you astray – the time lost could be detrimental to you iterating quickly enough to be successful. We’ve seen this time and time again. A data point that seemingly tells a story about how your game is doing, how well you’re monetizing your players, how strong your acquisition efforts are. So, let me caution you before you too ski off a cliff like this guy. Be careful! Achtung!

Here’s a client we recently helped through a situation they had encountered.Their % paying users, one of the 7 most important metrics, were steadily rising. In fact, recently the ratio was growing. A great sign that they were succeeding in building a game that they had figured out how to monetize. % Paying Users is a metric you want to see growing.However, on closer inspection, as their % paying users was increasing, their ARPPU was shrinking. So, for every paying user they had, they were netting less money.This trend might be OK, but their ARPPU was shrinking at a greater rate than their % paying users was growing, and as a result their Revenue was down. They had been lowering prices of their virtual goods, which attracted more buyers, but the price per unit couldn’t sustain healthy revenue growth.Don’t look at anything in isolation…when you point down the mountain, don’t get tunnel vision – there could be a tree ahead (or a cliff). Need a wholistic view of your application’s performance. If your arppu is $1,000 and you have only 1 paying user, not so good.

Process: follow the scientific process…hypothesis, then data analysis. What are the questions you want to ask? How do you test hypotheses? How do you get the answer? Need to do a top-down analysis.

Needs better design.

You’ve built a game, but need to prove there is a market for it, and determine what your scaling potential is. This is step number one in the data-driven design cycle.How should I segment my marketing campaigns to get visibility into different groups of users?Which regions should I target / test? By Marketing Channel: Which user segmentsAre the most influential?Are the most engaged? Are the most avid? Are the highest monetizing? ARM metrics to focus on in this stage: - CAC - 1-day retention - 7-day retention - Avg. Session Length

OK, congratulations, you’ve proven you’ve got a game that can monetize, now it’s time to rev up acquisition efforts. Presumably you know what types of marketing you need to do to generate high quantity installs…but how about quality? Can you get people engaged? How well can you monetize? Here are some key questions to be asking at this stage:Who are my most engaged users? What is the behavior of my paying vs. non-paying users?What is the behavior of my new vs. returning users? What are the critical chokepoints in my app I need to unblock? What user cohorts are the most engaged and influential? How can I get more users deeper into the app where they will start monetizing?What game mechanics can I test to increase virality, engagement and retention?

This is the HARVEST stage…Your game is monetizing efficiently, you’ve figured out the “system” and you want to keep it going as long as possible. It’s been fairly well proven that every game has a shelf-life on Facebook and other social networks…it’s the good ones that demand high engagement and strong monetization that last the longest.Big questions and learnings in this stage include:What is the behavior of my paying vs. non-paying users?What is the LTV of my users by different cohorts? When do my users start monetizing? How can I keep my paying users engaged and happy? How can I optimize my basket of virtual goods?How can I optimize the prices of my virtual goods? What is the behavior of my users of their entire lifetime?

Transcript

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Who is Kontagent?• Leading Analytics Solution for Social & Mobile Apps• Over 1.5B msgs handled/day• Track $1 of every $4 spent in the social gaming industry• 75+ employees across four countries• 150M+ MAUs tracked 3